University of Minnesota
School of Physics & Astronomy

MN Institute for Astrophysics Colloquium

Friday, December 1st 2017
Speaker: Shea Brown, U of Iowa
Subject: Astrophysical Machine Learning: Applications for the next generation all-sky surveys
Refreshments to be served in the MIfA Interaction Area (Tate 285-11) following the colloquium.

Machine learning is playing an ever increasing role in astrophysics, due in part to the large number of all-sky astronomical surveys that are being carried out in this decade. Massive data-rates make it effectively impossible for humans to individually inspect all the data, so we must rely on "intelligent" algorithms to classify scientifically relevant and exciting sources. I will highlight a few areas where machine learning, and in particular deep-learning, can play a significant role in upcoming surveys at radio wavelengths, leading up to the planned Square Kilometre Array.

Faculty Host: Lawrence Rudnick

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